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Record W4391379502 · doi:10.1177/10778012241228286

Challenges to the Provision of Services for Sexual and Intimate Partner Violence in Canada During the COVID-19 Pandemic: Results of a Nationwide Web-Based Survey

2024· article· en· W4391379502 on OpenAlexaffabout
Sonia Michaelsen, Sonia Parra Jordan, Christina Zarowsky, Alissa Koski

Bibliographic record

VenueViolence Against Women · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcGill UniversityUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsDomestic violenceStaffingPandemicBusinessCoronavirus disease 2019 (COVID-19)Mental healthPoison controlOccupational safety and healthSuicide preventionService delivery frameworkVulnerability (computing)Public relationsService (business)MedicineMedical emergencyNursingPolitical scienceComputer securityPsychiatryMarketing

Abstract

fetched live from OpenAlex

The COVID-19 pandemic increased women's vulnerability to intimate partner and sexual violence (IPV/SV), as well as challenging organizations' ability to respond. This research is based on a 2021 nationwide survey about the impacts of COVID-19 on IPV/SV services across Canada. Nationwide, organizations adopted several measures to reduce the risk of COVID-19 transmission, including scaling back services, reducing or stopping their volunteers, and reducing the number of in-shelter clients. Organizations detailed several financial challenges including increased costs and cancelation of fundraising events. Organizations also reported many staffing challenges, from increased workloads to staff leaves of absence due to childcare responsibilities, mental health reasons, or contracting COVID-19. Policies ensuring adequate financial support to IPV/SV services in nonemergency times could help minimize disruption to service delivery during crisis situations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.335
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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